Terni

• Distribuzione traccianti

pltnts <- list.files("~/R/terni/rds", pattern = "*.rds", full.names = TRUE) 

map(pltnts, \(pltnt) {
  inquinante <- tools::file_path_sans_ext(basename(pltnt))
  
  cat("\n## ", inquinante, "\n\n")

  df <- read_csv("~/R/terni/data/dataframes/df_finale.csv", show_col_types = FALSE)
  index <- grep(inquinante, names(df))
  names(df)[index] <- "value"

  rds <- readRDS(pltnt)
  mod <- getModel(names(rds), df)
  
  gamtabs(mod, type = "HTML")
  cat("\n\n")
    
  cat("R²:", summary(mod)$r.sq %>% round(3) )
cat("\n\n")
  # stargazer(mod, type="text" )

  appraise(mod) %>% print()
  draw(mod) %>% print()
  
  rm(mod)
  cat("\n\n")
})

Al_i

stringa modello: gam(log(value) ~ s(tp_max) + s(t2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.6455	0.0048	345.0029	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(tp_max)	4.0117	4.5928	16.0655	< 0.0001
s(t2m_IQR)	1.3790	1.5696	9.1918	0.0004

R²: 0.275

Al_s

stringa modello: gam(log(value) ~ s(pbl12_min) + s(scrapyard) + s(sp_IQR) + s(wspeed_max_max) + s(tp_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.6054	0.0168	36.0994	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl12_min)	1.0001	1.0002	18.4363	< 0.0001
s(scrapyard)	3.9608	4.8647	7.4353	< 0.0001
s(sp_IQR)	1.0000	1.0001	58.1864	< 0.0001
s(wspeed_max_max)	2.4165	2.8309	20.3789	< 0.0001
s(tp_median)	1.8030	1.9107	6.0010	0.0028

R²: 0.647

As_i

stringa modello: gam(log(value) ~ s(pbl12_median) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.7978	1099012885.8576	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl12_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

As_s

stringa modello: gam(log(value) ~ s(cold_area) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-24.3739	1470767797.3404	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(cold_area)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

B_i

stringa modello: gam(log(value) ~ s(pwspeed_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-8.2656	72.9295	-0.1133	0.9098
B. smooth terms	edf	Ref.df	F-value	p-value
s(pwspeed_IQR)	4.9512	5.0933	3.7966	0.0020

R²: 0.39

B_s

stringa modello: gam(log(value) ~ s(wspeed_max) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.9984	1108.9985	-0.0216	0.9828
B. smooth terms	edf	Ref.df	F-value	p-value
s(wspeed_max)	8.0000	8.0001	6.7448	< 0.0001

R²: 0.494

Ba_i

stringa modello: gam(log(value) ~ s(pbl00_IQR) + s(imp_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-0.7146	234.0267	-0.0031	0.9976
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl00_IQR)	8.0001	8.0001	17.0090	< 0.0001
s(imp_200)	1.0000	1.0000	6.0656	0.0144

R²: 0.68

Ba_s

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(s8_sup_200) + s(pop_200) + s(pbl12_max) + s(wspeed_IQR) + s(wspeed_max) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.3907	0.0170	23.0436	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl12_mean)	1.0000	1.0001	0.4034	0.5259
s(s8_sup_200)	2.4015	3.0389	10.0470	< 0.0001
s(pop_200)	1.0000	1.0000	9.1488	0.0027
s(pbl12_max)	4.3058	5.0450	11.0489	< 0.0001
s(wspeed_IQR)	1.0000	1.0001	110.4326	< 0.0001
s(wspeed_max)	1.8379	2.1012	11.7025	< 0.0001

R²: 0.658

Bi_i

stringa modello: gam(log(value) ~ s(wdir_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.3786	728888923.6442	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(wdir_min)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Bi_s

stringa modello: gam(log(value) ~ s(kndvi) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.7952	820896902.7941	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(kndvi)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Ca_i

stringa modello: gam(log(value) ~ s(pblmin_max) + s(tp_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.9101	0.0047	407.1479	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmin_max)	1.0001	1.0002	36.5603	< 0.0001
s(tp_IQR)	4.0549	4.5367	26.1999	< 0.0001

R²: 0.451

Ca_s

stringa modello: gam(log(value) ~ s(nirradiance_IQR) + s(bh_200) + s(s8_sup_200) + s(tmin2m_max) + s(s6_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.8921	0.0027	688.8894	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(nirradiance_IQR)	2.2636	2.6515	58.4584	< 0.0001
s(bh_200)	3.8818	4.6559	6.5691	< 0.0001
s(s8_sup_200)	5.2758	6.2124	5.8510	< 0.0001
s(tmin2m_max)	2.3224	2.8845	8.0398	< 0.0001
s(s6_sup_200)	1.0002	1.0004	8.4140	0.0040

R²: 0.529

Cd_i

stringa modello: gam(log(value) ~ s(s5_sup_200, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.4419	448071431.0465	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(s5_sup_200)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Cd_s

stringa modello: gam(log(value) ~ s(v10m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.5796	387687954.4849	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(v10m_min)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Ce_i

stringa modello: gam(log(value) ~ s(s7_sup_200, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.4443	464327192.8477	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(s7_sup_200)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Ce_s

stringa modello: gam(log(value) ~ s(t2m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.4449	575094374.2699	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(t2m_min)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Co_i

stringa modello: gam(log(value) ~ s(cold_area) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-69.3581	262156066.4195	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(cold_area)	1.0000	1.0000	0.0000	1.0000

R²: 0.053

Co_s

stringa modello: gam(log(value) ~ s(kndvi) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.8396	653381530.1422	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(kndvi)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Cr_i

stringa modello: gam(log(value) ~ s(cold_area) + s(wdir_IQR) + s(s6_sup_200) + s(u10m_median, k=9) + s(s1_sup_200, k=7) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.1558	0.0069	166.5089	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(cold_area)	4.6024	5.4578	61.4565	< 0.0001
s(wdir_IQR)	1.0000	1.0001	150.3107	< 0.0001
s(s6_sup_200)	7.1140	7.9155	8.7323	< 0.0001
s(u10m_median)	1.0001	1.0001	46.2379	< 0.0001
s(s1_sup_200)	1.6618	1.8638	3.8679	0.0136

R²: 0.744

Cs_i

stringa modello: gam(log(value) ~ s(wdir_max) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.1515	404332678.1999	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(wdir_max)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Cs_s

stringa modello: gam(log(value) ~ s(wspeed_min, k=8) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.6596	563533184.0972	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(wspeed_min)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Cu_i

stringa modello: gam(log(value) ~ s(pblmax_mean) + s(s5_sup_200, k=9) + s(s6_sup_200) + s(m_dis_ferr) + s(s3_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.7405	0.0091	81.4914	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmax_mean)	4.3271	5.1926	76.1385	< 0.0001
s(s5_sup_200)	7.4404	7.7783	13.3667	< 0.0001
s(s6_sup_200)	1.1743	1.3090	5.8071	0.0080
s(m_dis_ferr)	1.0001	1.0002	26.4269	< 0.0001
s(s3_sup_200)	4.1164	4.6546	7.7620	< 0.0001

R²: 0.678

Cu_s

stringa modello: gam(log(value) ~ s(nirradiance_mean) + s(hot_area) + s(m_dis_ferr) + s(s4_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-0.2545	0.0394	-6.4615	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(nirradiance_mean)	7.9657	8.4567	26.1746	< 0.0001
s(hot_area)	8.6498	8.9592	9.3167	< 0.0001
s(m_dis_ferr)	1.0001	1.0001	10.0428	0.0017
s(s4_sup_200)	1.6659	1.9615	3.9791	0.0361

R²: 0.651

Fe_i

stringa modello: gam(log(value) ~ s(wdir_IQR) + s(cold_area) + s(wspeed_max_mean) + s(s5_sup_200, k=9) + s(s3_sup_200) + s(s4_sup_200) + s(s1_sup_200, k=7) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.8038	0.0032	558.8771	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(wdir_IQR)	3.7184	4.5237	20.0079	< 0.0001
s(cold_area)	8.9648	8.9971	26.8025	< 0.0001
s(wspeed_max_mean)	1.0000	1.0001	19.7659	< 0.0001
s(s5_sup_200)	1.0000	1.0000	33.8379	< 0.0001
s(s3_sup_200)	1.0000	1.0000	22.1570	< 0.0001
s(s4_sup_200)	1.9781	2.2997	5.6069	0.0033
s(s1_sup_200)	1.0000	1.0001	5.9050	0.0158

R²: 0.637

Fe_s

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(m_dis_ferr) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.7988	0.0150	53.1779	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl12_mean)	8.8137	8.9888	27.1550	< 0.0001
s(m_dis_ferr)	1.9068	2.3652	7.6610	0.0004

R²: 0.61

Ga_i

stringa modello: gam(log(value) ~ s(v10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.4915	399086832.8146	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(v10m_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Ga_s

stringa modello: gam(log(value) ~ s(v10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.8122	885536862.8724	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(v10m_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

K_i

stringa modello: gam(log(value) ~ s(pbl00_IQR) + s(pwspeed_min, k=7) + s(pblmin_max) + s(s6_sup_200) + s(pwspeed_IQR) + s(nirradiance_min) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.8271	0.0067	272.6002	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl00_IQR)	1.5539	1.7218	21.4260	< 0.0001
s(pwspeed_min)	1.0000	1.0000	135.6251	< 0.0001
s(pblmin_max)	3.1130	3.4339	32.5927	< 0.0001
s(s6_sup_200)	1.1279	1.2438	5.2072	0.0131
s(pwspeed_IQR)	2.0066	2.2014	4.7931	0.0136
s(nirradiance_min)	1.0001	1.0002	32.4704	< 0.0001

R²: 0.727

K_s

stringa modello: gam(log(value) ~ s(sp_max) + s(sp_mean) + s(nirradiance_IQR) + s(tmax2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.6976	0.0046	368.3042	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(sp_max)	1.0000	1.0001	354.5608	< 0.0001
s(sp_mean)	1.9732	2.4231	2.1526	0.0695
s(nirradiance_IQR)	1.0013	1.0025	12.5933	0.0004
s(tmax2m_IQR)	1.0000	1.0000	12.0815	0.0006

R²: 0.792

La_i

stringa modello: gam(log(value) ~ s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.4328	658563871.6587	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmin_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

La_s

stringa modello: gam(log(value) ~ s(pblmin_IQR, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.9853	1033837054.2196	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmin_IQR)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Li_i

stringa modello: gam(log(value) ~ s(s7_sup_200, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.1998	628699215.4837	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(s7_sup_200)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Li_s

stringa modello: gam(log(value) ~ s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.0640	526087096.1012	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmin_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Mg_i

stringa modello: gam(log(value) ~ s(nirradiance_IQR) + s(pblmin_median, k=3) + s(sp_IQR) + s(v10m_min) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.3775	0.0049	283.4954	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(nirradiance_IQR)	2.4391	2.8824	12.5031	< 0.0001
s(pblmin_median)	1.0000	1.0000	23.8863	< 0.0001
s(sp_IQR)	1.0001	1.0001	132.3558	< 0.0001
s(v10m_min)	1.0000	1.0000	6.7063	0.0101

R²: 0.425

Mg_s

stringa modello: gam(log(value) ~ s(pbl00_max) + s(scrapyard) + s(wspeed_max_mean) + s(s3_sup_200) + s(s6_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.4034	0.0042	332.7256	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl00_max)	4.8392	5.4881	22.2652	< 0.0001
s(scrapyard)	1.4159	1.7132	24.7618	< 0.0001
s(wspeed_max_mean)	2.7679	3.1948	6.2116	0.0003
s(s3_sup_200)	1.9556	2.3266	3.4066	0.0276
s(s6_sup_200)	1.0001	1.0002	6.0819	0.0143

R²: 0.509

Mn_i

stringa modello: gam(log(value) ~ s(s3_sup_200) + s(wdir_IQR) + s(nirradiance_max) + s(cold_area) + s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.7850	0.0082	95.3319	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(s3_sup_200)	7.6200	7.7565	19.5203	< 0.0001
s(wdir_IQR)	1.0000	1.0000	60.7915	< 0.0001
s(nirradiance_max)	1.0000	1.0000	29.9658	< 0.0001
s(cold_area)	7.4337	8.1583	5.4911	< 0.0001
s(pblmin_median)	1.0000	1.0000	5.7412	0.0173

R²: 0.653

Mn_s

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(scrapyard) + s(nirradiance_IQR) + s(wspeed_max) + s(s7_sup_200, k=3) + s(s6_sup_200) + s(cold_area) + s(pwspeed_max) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.5601	0.0114	49.0079	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl12_mean)	1.0000	1.0001	100.1215	< 0.0001
s(scrapyard)	1.0000	1.0000	44.9631	< 0.0001
s(nirradiance_IQR)	2.3936	2.7712	67.2165	< 0.0001
s(wspeed_max)	0.9928	1.2275	1.8230	0.2093
s(s7_sup_200)	1.0000	1.0000	6.0225	0.0148
s(s6_sup_200)	1.1998	1.3712	9.0031	0.0031
s(cold_area)	1.0000	1.0000	13.4237	0.0003
s(pwspeed_max)	1.9928	2.2275	2.2804	0.1033

R²: 0.705

Mo_i

stringa modello: gam(log(value) ~ s(pblmax_min) + s(cold_area) + s(s1_sup_200, k=7) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-0.2418	0.0974	-2.4819	0.0137
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmax_min)	7.8757	8.3728	22.1125	< 0.0001
s(cold_area)	2.8465	3.5748	26.8501	< 0.0001
s(s1_sup_200)	1.7710	1.9837	5.6332	0.0028

R²: 0.668

Mo_s

stringa modello: gam(log(value) ~ s(cold_area) + s(nirradiance_mean) + s(s3_sup_200) + s(pblmin_median, k=3) + s(m_dis_ferr) + s(pbl00_mean) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.4577	0.0168	27.2783	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(cold_area)	3.5956	4.3740	34.6549	< 0.0001
s(nirradiance_mean)	2.6442	3.2531	33.3174	< 0.0001
s(s3_sup_200)	7.5440	7.8310	14.9215	< 0.0001
s(pblmin_median)	1.0000	1.0000	27.9580	< 0.0001
s(m_dis_ferr)	1.0001	1.0002	11.5412	0.0008
s(pbl00_mean)	1.0000	1.0000	9.5925	0.0022

R²: 0.755

Na_i

stringa modello: gam(log(value) ~ s(pwspeed_min, k=7) + s(v10m_mean) + s(tmax2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.7114	0.0048	360.1878	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pwspeed_min)	1.0000	1.0001	61.0800	< 0.0001
s(v10m_mean)	1.0000	1.0000	18.3960	< 0.0001
s(tmax2m_IQR)	1.0000	1.0000	8.6452	0.0036

R²: 0.203

Na_s

stringa modello: gam(log(value) ~ s(u10m_IQR) + s(s7_sup_200, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.7578	0.0035	505.0096	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(u10m_IQR)	8.0000	8.0000	39.9308	< 0.0001
s(s7_sup_200)	1.8868	1.9872	5.6379	0.0044

R²: 0.562

Nb_i

stringa modello: gam(log(value) ~ s(rh_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.4201	969820731.2887	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(rh_min)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Nb_s

stringa modello: gam(log(value) ~ s(v10m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.6778	916551596.4583	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(v10m_min)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Ni_i

stringa modello: gam(log(value) ~ s(cold_area) + s(wdir_IQR) + s(s6_sup_200) + s(s1_sup_200, k=7) + s(wdir_median) + s(t2m_IQR) + s(sp_mean) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.8086	0.0120	67.2528	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(cold_area)	4.7186	5.5927	57.8875	< 0.0001
s(wdir_IQR)	1.0000	1.0000	47.3159	< 0.0001
s(s6_sup_200)	6.9075	7.7226	6.2336	< 0.0001
s(s1_sup_200)	1.3748	1.5519	4.1753	0.0135
s(wdir_median)	1.0000	1.0000	29.2577	< 0.0001
s(t2m_IQR)	1.0000	1.0000	10.7289	0.0012
s(sp_mean)	1.0000	1.0000	7.1496	0.0080

R²: 0.722

Ni_s

stringa modello: gam(log(value) ~ s(m_dis_ferr) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-2171.6198	822.9713	-2.6388	0.0088
B. smooth terms	edf	Ref.df	F-value	p-value
s(m_dis_ferr)	1.0000	1.0000	6.9647	0.0088

R²: 0.231

Pb_i

stringa modello: gam(log(value) ~ s(nirradiance_mean) + s(s3_sup_200) + s(wspeed_IQR) + s(wspeed_max_max) + s(wspeed_max_min) + s(wdir_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.3775	0.0140	26.9697	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(nirradiance_mean)	1.0000	1.0000	32.1711	< 0.0001
s(s3_sup_200)	7.4182	7.8474	28.0831	< 0.0001
s(wspeed_IQR)	1.0000	1.0000	0.2321	0.6304
s(wspeed_max_max)	2.3873	2.8503	6.3126	0.0006
s(wspeed_max_min)	1.0000	1.0000	12.3599	0.0005
s(wdir_IQR)	1.0000	1.0000	6.7340	0.0100

R²: 0.673

Pb_s

stringa modello: gam(log(value) ~ s(sp_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.7611	2017.3248	-0.0113	0.9910
B. smooth terms	edf	Ref.df	F-value	p-value
s(sp_min)	1.0000	1.0001	0.0005	0.9891

R²: 0.237

PM10

stringa modello: gam(log(value) ~ s(v10m_min) + s(pwspeed_IQR) + s(v10m_median, k=9) + s(pop_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.2172	0.0039	314.5843	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(v10m_min)	2.0134	2.3286	21.4008	< 0.0001
s(pwspeed_IQR)	3.9684	4.4831	68.1847	< 0.0001
s(v10m_median)	1.0001	1.0002	24.7028	< 0.0001
s(pop_200)	1.0001	1.0001	6.1866	0.0135

R²: 0.754

Rb_i

stringa modello: gam(log(value) ~ s(pblmin_IQR, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.8260	713180328.4144	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmin_IQR)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Rb_s

stringa modello: gam(log(value) ~ s(tmax2m_mean) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-9.6249	9.3176	-1.0330	0.3025
B. smooth terms	edf	Ref.df	F-value	p-value
s(tmax2m_mean)	1.2578	1.4561	0.8830	0.2305

R²: 0.323

Sb_i

stringa modello: gam(log(value) ~ s(rh_max) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-131.5374	349025.3924	-0.0004	0.9997
B. smooth terms	edf	Ref.df	F-value	p-value
s(rh_max)	1.0001	1.0002	0.0001	0.9955

R²: 0.124

Sb_s

stringa modello: gam(log(value) ~ s(tmin2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-24.6092	1429869567.4169	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(tmin2m_IQR)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Sn_i

stringa modello: gam(log(value) ~ s(pbl12_mean) + s(hot_area) + s(s4_sup_200) + s(s8_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-0.3424	0.0494	-6.9266	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl12_mean)	1.0000	1.0000	332.2339	< 0.0001
s(hot_area)	2.1122	2.6346	19.1946	< 0.0001
s(s4_sup_200)	1.0001	1.0001	7.2134	0.0077
s(s8_sup_200)	1.6335	2.0390	5.9573	0.0029

R²: 0.779

Sn_s

stringa modello: gam(log(value) ~ s(pblmin_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.0719	467030630.5307	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pblmin_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Sr_i

stringa modello: gam(log(value) ~ s(tp_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-2.9084	5.5494	-0.5241	0.6006
B. smooth terms	edf	Ref.df	F-value	p-value
s(tp_IQR)	7.0532	7.1164	3.4669	0.0007

R²: 0.441

Sr_s

stringa modello: gam(log(value) ~ s(v10m_median, k=9) + s(imp_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-1.6333	102.5148	-0.0159	0.9873
B. smooth terms	edf	Ref.df	F-value	p-value
s(v10m_median)	7.0000	7.0001	23.6825	< 0.0001
s(imp_200)	1.0000	1.0000	6.9530	0.0089

R²: 0.61

Ti_i

stringa modello: gam(log(value) ~ s(rh_max) + s(pop_200) + s(pblmin_IQR, k=9) + s(s3_sup_200) + s(s4_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	0.4315	0.0140	30.7584	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(rh_max)	3.1428	3.8193	39.0249	< 0.0001
s(pop_200)	6.4832	7.1177	8.2150	< 0.0001
s(pblmin_IQR)	1.0001	1.0001	42.6792	< 0.0001
s(s3_sup_200)	3.8167	4.1252	4.0033	0.0017
s(s4_sup_200)	1.0001	1.0001	17.1647	< 0.0001

R²: 0.528

Ti_s

stringa modello: gam(log(value) ~ s(u10m_mean) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-23.7015	1110537246.7412	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(u10m_mean)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Tl_i

stringa modello: gam(log(value) ~ s(s5_sup_200, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.8890	910243221.4849	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(s5_sup_200)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Tl_s

stringa modello: gam(log(value) ~ s(s3_sup_200) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.5587	419614258.4032	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(s3_sup_200)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

U_i

stringa modello: gam(log(value) ~ s(kndvi) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.2572	573815329.4478	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(kndvi)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

U_s

stringa modello: gam(log(value) ~ s(tp_median, k=3) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.2573	670082786.1382	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(tp_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

V_i

stringa modello: gam(log(value) ~ s(tmin2m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-24.8338	1374423269.9621	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(tmin2m_IQR)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

V_s

stringa modello: gam(log(value) ~ s(t2m_min) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

## Warning in newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L,
## : Adattamento terminato con errore di passo: controllare attentamente i
## risultati

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-42.5595	68.3869	-0.6223	0.5342
B. smooth terms	edf	Ref.df	F-value	p-value
s(t2m_min)	1.0000	1.0000	0.3700	0.5435

R²: 0.131

W_i

stringa modello: gam(log(value) ~ s(u10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.5540	418208955.1281	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(u10m_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

W_s

stringa modello: gam(log(value) ~ s(u10m_median, k=9) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.7686	503187178.7599	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(u10m_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Zn_i

stringa modello: gam(log(value) ~ s(wdir_mean) + s(nirradiance_max) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.2598	0.0071	176.2748	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(wdir_mean)	6.8044	7.6989	22.9497	< 0.0001
s(nirradiance_max)	1.0001	1.0002	3.0862	0.0801

R²: 0.41

Zn_s

stringa modello: gam(log(value) ~ s(nirradiance_mean) + s(scrapyard) + s(v10m_IQR) , gamma=1.4, family=gaussian(link=log), data = df)

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.1164	0.0075	148.6038	< 0.0001
B. smooth terms	edf	Ref.df	F-value	p-value
s(nirradiance_mean)	4.9428	5.4388	51.9507	< 0.0001
s(scrapyard)	3.2254	4.0040	9.9808	< 0.0001
s(v10m_IQR)	3.0812	3.6322	26.1013	< 0.0001

R²: 0.754

Zr_i

stringa modello: gam(log(value) ~ s(sp_median) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-24.5748	935817661.1736	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(sp_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

Zr_s

stringa modello: gam(log(value) ~ s(pbl00_median, k=6) , gamma=1.4, family=gaussian(link=log), data = df)

## Warning in log(mu): Si è prodotto un NaN

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	-22.7011	677221618.5782	-0.0000	1.0000
B. smooth terms	edf	Ref.df	F-value	p-value
s(pbl00_median)	1.0000	1.0000	0.0000	1.0000

R²: -0.004

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